from fastapi import FastAPI, Response
from fastapi.responses import FileResponse
from kokoro import KPipeline
import soundfile as sf
import os
import numpy as np
import torch 

app = FastAPI()

# Initialize pipeline once at startup
pipeline = KPipeline(lang_code='a')

@app.post("/generate")
async def generate_audio(text: str, voice: str = "af_heart", speed: float = 1.0):
    # Generate audio
    generator = pipeline(
        text, 
        voice=voice,
        speed=speed,
        split_pattern=r'\n+'
    )
    
    # # Save first segment only for demo
    # for i, (gs, ps, audio) in enumerate(generator):
    #     sf.write(f"output_{i}.wav", audio, 24000)
    #     return FileResponse(
    #         f"output_{i}.wav",
    #         media_type="audio/wav",
    #         filename="output.wav"
    #     )
    
    # return Response("No audio generated", status_code=400)


    # Process only the first segment for demo
    for i, (gs, ps, audio) in enumerate(generator):

        # Convert PyTorch tensor to NumPy array
        audio_numpy = audio.cpu().numpy()
        # Convert to 16-bit PCM
        
        # Ensure the audio is in the range [-1, 1]
        audio_numpy = np.clip(audio_numpy, -1, 1)
        # Convert to 16-bit signed integers
        pcm_data = (audio_numpy * 32767).astype(np.int16)
        
        # Convert to bytes (automatically uses row-major order)
        raw_audio = pcm_data.tobytes()
        
        # Return PCM data with minimal necessary headers
        return Response(
            content=raw_audio,
            media_type="application/octet-stream",
            headers={
                "Content-Disposition": f'attachment; filename="output.pcm"',
                "X-Sample-Rate": "24000",
                "X-Bits-Per-Sample": "16",
                "X-Endianness": "little"
            }
        )
    
    return Response("No audio generated", status_code=400)